فهرست مطالب masoud rabbani

One of the biggest challenges of the 21st century is meeting the needs of the growing world population. The supply chain of perishable goods, including food, dairy products, medicines, and blood products, has recently received attention due to their impact on human life. In this article, the design of a sustainable supply chain network for perishable items has been discussed. To optimize this supply chain, a multi-objective Mixed Integer Linear Programming (MILP) model has been developed to formulate the problem. Fixed deterioration rate (expiration date) is considered. To perform the research calculations, GAMS software and the combined method of Bander's analysis and Lagrange coefficient were used, and based on the data, results were obtained, and the relative weight of the stability of the solution (ω) was equal to 0.5 and the relative weight of the stability of the model was (ω) equal to 5000 has been developed to meet the proposed objectives. These comparisons show that the presented network was robust in all performance objectives. The results obtained from the combined method regarding the three objective functions defined for the main model show this fact. The results of the first iteration provide us with better answers compared to the other iterations.Originality/Value: This research can be considered as one of the first optimization papers that presented a multi-level and multi-product-multi-period supply chain with uncertainty in the parameters in the dairy and pharmaceutical industries and the environmental costs of production and transportation and sustainable social costs such as reported accidents and incidents, job satisfaction, safety, reduction of dispatch time and lost working days simultaneously with the economic dimension in management-related decisions. Allocation combines location and routing.

This paper proposes a bi-objective model for a green closed-loop supply chain network design. Four levels for forward and five levels for reverse flow were considered, including plants, distribution centers, online retailers, traditional retailers and customers for forward flow and customers, collecting centers, disposal centers, repair centers and plants for the reverse flow. The objectives are minimizing the GHG emission and maximizing profit by considering defective products and a second market for these products. Also, online retailers were considered alongside with traditional ones, since the Covid-19 pandemic has led to increase in the amount of online shopping. GAMS software and the Lpmetric technique were used to solve the model in the small and medium sizes. However, for the large size, we used Grasshopper Optimization Algorithm (GOA) as a meta-heuristic approach since solving the large size problem with GAMS is a complicated and time-consuming process. We provided Numerical and computational results to prove the efficiency and feasibility of the presented model. Finally, the managerial insights and future works were provided.

In healthcare, the Patient Appointment Scheduling (PAS) problem is one of the critical issues in Outpatient Chemotherapy Clinics (OCC). In the wake of this, this paper proposes a novel bi-objective mathematical programming model for solving the PAS problem in OCC. The developed mathematical model is inspired by cellular manufacturing. The first objective function minimizes the completion time of all treatments, and the second objective function maximizes the use of nurses' skills while considering clustered patients about their characteristics. To solve the bi-objective mathematical model, for the first time a hybrid approach based on Torabi-Hassini (TH) and Lagrange method is utilized. The results indicate that an increase in the number of nurses will enhance the treatment completion speed and allocation of nurses’ work skill. On the other hand, an increase in the number of chairs in clinics will decrease the assignments of nurses’ skills priority. The study supports decision makers in considering nurses' skills for the PAS problem. The results also denote the desirability of the proposed model. To validate the proposed model, OCC in Tehran is considered as a case study. Computational results reveal that considering nurses' skills in OCC and using the fuzzy clustering model, as a new method in patient clustering, lead to achieving a desirable and more realistic outcome.

Prostate cancer is the most common malignancy in men and the second leading cause of death in all countries of the world. According to the US statistics, about 27% of men in the United States were diagnosed with prostate cancer in 2014, and 10% will die of cancer. Over the past decades, prostate cancer management has reached a point where the degree, stage, and location of cancer determine the type of treatment. Treatment for prostate cancer depends on the spread of cancer at the time of diagnosis. Several clinical trials, including the digital rectal examination (DRE), biochemistry such as prostate-specific Antigen (PSA), and pathology such as ultrasound-guided biopsy (TRUS) are used to assess the size and spread of prostate cancer. However, these tests alone are not enough and reliable to diagnose and stage prostate cancer. In addition to the above methods, there are extensive imaging techniques for the diagnosis and staging of prostate tumors. Initially, CT scans were used for staging, but due to the inherent weakness of CT scans in differentiating prostate disease, MRI and ultrasound were used to diagnose and determine the of stage prostate cancer. Although, the standard method for diagnosing and staging prostate cancer is sampling and determining the Gleason index. In this study, the relationship between Gleason score as a standard method in patients with prostate cancer was compared with the parameters extracted from Diffusion-weighted imaging (DWI). In the DWI method, based on the diffusion of water molecules among intracellular molecules, a parameter called the apparent diffusion coefficient (ADC) is determined in the images and can provide precise contrast to differentiate tumor cells from healthy cells. Sensitivity to the diffusion of water molecules is defined by the gradient factor b (b-value) in s / mm2. This factor indicates the intensity and time of applying gradients to produce diffusion-weighted images. The higher b-value, the stronger effect of the diffusion of water molecules and results in better contrast, greater propagation and lower T2 shine-through effect, but a lower signal-to-noise ratio and a higher sensitivity artifact. Magnetic Resonance Imaging (MRI) provides information on the biological behavior of prostate cancer tissue in diffusion weighted (DW) MR images. The diffusion rate measured by DWI is related to the average path length traveled by water molecules so that the longer path leads to an increase in ADC. The three-dimensional diffusion of water molecules into the tissue is not random due to the presence of cell membrane impedance. Also in tissues with high cell density, increasing cell membrane may significantly inhibit the diffusion of intercellular water molecules and reduce the diffusion pathway and hence reduce ADC. Therefore, this parameter can indirectly provide information about tissue cells. The anatomical structure of normal prostate tissue consists of a glandular component associated with stroma tissue. Water molecules are easily diffused into the glandular component of prostate tissue. But as cancer becomes more distinct, the cell density increases, and the glandular structure becomes unrecognizable.

This fundamental study was performed on 90 patients with prostate cancer, according to McDonald's criteria, who were referred to Shafa Imaging Center in Isfahan, from March 2020 to January 2021. First, the informed consent form was obtained from all patients and then they participated in the study. In this study, patients were first referred to the MRI imaging department and routine MRI protocols were performed on them. Then, in order to evaluate the accuracy of the MRI-diffusion imaging method in comparison with the surgical and pathological results of patients and also to determine the optimal value of the resulting parameters, this sequence was performed on patients to evaluate the extent of tumor invasion. Free-breathing DW MR axial images were obtained using Siemens Avanto system 1.5 Tesla (with b-values equal to 0, 1000, 1500, and 2000 mm2 / s). The European Society of Urogenital Radiology (ESUR) recommends a b-value in the range of 800-1000 mm2 / s for prostate imaging. ROIs were plotted independently on ADC maps. To measure ADC in prostate cancer, tumor areas on T2W images were used to measure ADC, which accounted for a total of 24 ADC measurements in all three diffusion gradients for each patient. ADC values were measured by inserting ROI with an average area of 30 mm2. Using Medcalc software version 15 with a 95% confidence level, the indicators of sensitivity, specificity, accuracy, threshold, positive, and negative predictive value were calculated.

The results showed that in 6 patients with Gleason score 6, the mean value of ADC using 32 ROIs (Region of Interests) was 0.982 ± 0.909 SD s/mm2. In 9 patients with Gleason score 7, the mean ADC value using 52 ROIs was 0.961 ± 0.806 SD s/mm2. In 6 patients with Gleason score 8, the mean ADC value using 52 ROIs was 0.924 ± 0.786 SD s/mm2. In 4 patients with Gleason score 9, the mean ADC value using 24 ROIs was 0.812 ± 0.726 SD s/mm2. Pearson correlation analysis showed a significant negative correlation between Gleason score and ADC measurements (r = -0.926, P = 0.01).

According to the guidelines of the European association of urology in 2013, the main tools for diagnosing prostate cancer include DRE (Digital Rectal Examination), PSA (Prostate Specific Antigen) and TRUS (Trans-Rectal Ultrasonography)-guided biopsy. Prostate cancer is often multifocal. It is generally accepted that GS determines the prognosis, the lesion with the highest GS has the weakest prognosis. The correlation of imaging with histopathology is very important for the validation and creation of new imaging biomarkers. In particular, accurate correlation allows to analyze relationships between quantitative MRI-based parameters and histopathology, and allows to evaluate the accuracy of imaging in tumor examination. The main limitation of previous studies was the unreliability of the amount of GS obtained from biopsy and problems in determining the exact location of the tumor on MRI. To overcome this limitation, prostatectomy specimens were used for the image and GS communication in this study. The results showed that there was a significant negative relationship between ADC values calculated from DW images and GS of prostate cancer obtained from a prostatectomy sample.

In large-scale emergency, the vehicle routing problem focuses on finding the best routes for vehicles. The equitable distribution has a vital role in this problem to decrease the number of death and save people's lives. In addition to this, air pollution is a threat to people’s life and it can be considered to omit other kinds of disasters happens because of it. So, a new MINLP model presented is going to face a real situation by considering real world assumptions such as fuzzy demands and travel time, multi depots and items, vehicle capacity and split delivery. The first objective function is to minimize the sum of unsatisfied demand which follows a piecewise function and the second one is to minimize the cost which depends on the fuel consumption. In order to solve the multi-objective problem with fuzzy parameters, nonlinear function has been linearized by convex combination and a new crisp model is presented by defusing fuzzy parameters. Finally, NSGA-П algorithm is applied to solve this problem and the numerical results gained by this procedure demonstrate its convergence and its efficiency in this problem.

Disaster situation could suddenly apply large number of injured people and disarrange the emergency medical service simultaneously (EMS). Using all facilities, ambulance, rescue helicopter and medical drone as integrated part and making decision for assigning to patient’s efficiency are concerns of EMS. therefore, in this study we introduce a new bi-objective model for EMS in order to reduce the rate of mobility and mortality with aim of reducing latest service completion time and total cost of system simultaneously. By recognition level of injury, we consider two types of patients: i. red code patients who have been injured seriously and have to be taken to an hospital. and ii. green code patients who are injured slightly and are treated at the same place. Since making decision and responding to disaster situation should be with high quality within seconds, for this purpose after validating and solving that by augmented ε-constraint method by GAMS, we applied two NSGA-II and customized Bees algorithm for multi objective solution that called Multi Objective Bees Algorithm (MOBA) in dynamics and uncertain situation for coping high frequency within very short response time and near the optimum solution result. The quality of the result is considered for choosing the metaheuristic solution. At the end, sensitive analysis is implemented on the model and the effect of reducing and increasing some of parameters on the model is investigated.

Vertical cooperative advertisement is one of the market strategies in which the manufacturer pays for a part of the supplier's local advertisements. Considering the quality level of products in the model and demand function is necessary and exciting. In this paper, a two-level supply chain with one manufacturer and one supplier, and one complementary manufacturer is developed, which advertisement costs share among manufacturer and retailer. It means that we have a cooperative advertisement in this model. The demand in this paper has uncertainty, and retailers order products from the manufacturer with uncertainty. This supply chain and distribution channel with uncertainty in retailers' demand, products quality level, and considering complementary price and qualities is one of the most practical models in this field. The main aim of these models is to give insights to managers and decision-makers by investigating the effect of the participation rate and some of the important parameters on other essential variables and profit functions and display the relationship between this rate and the trend of the variables. There are two non-cooperative games, including manufacturer Stackelberg and supplier Stackelberg. The results are obtained from these games, and the trends are demonstrated.

The current study was performed to compare susceptibility‑weighted imaging (SWI) with magnetic resonance imaging (MRI) methods of T2‑weighted (T2W) and fluid‑attenuated inversion recovery (FLAIR) imaging in multiple sclerosis (MS) plaque assessment.

This cross‑sectional study was conducted among 50 MS patients referred to Shafa Imaging Center, Isfahan, Iran. Patients who fulfilled McDonald criteria and were diagnosed with MS by a professional neurologist at least 1 year before the study initiation were included in the study. Eligible patients underwent brain scans using SWI, T2W imaging, and FLAIR. Plaques’ number and volume were detected separately for each imaging sequence. Moreover, identified lesions in SWI sequence were evaluated in terms of iron deposition and central veins.

Totally 50 patients (10 males and 40 females) with a mean age of 28.48 ± 5.25 years were included in the current study. Majority of patients (60%) had a disease duration of >5 years, and mean expanded disability status score was 2.56 ± 1.32. There was no significant difference between different imaging modalities in terms of plaques’ number and volume (P > 0.05). It was also found that there was a high correlation between SWI and conventional imaging techniques of T2W (r = 0.97, 0.91, P < 0.001) and FLAIR (r = 0.99, 0.99, P < 0.001) in the estimation of both the number and volume of plaques (P < 0.001).

The results of the present study indicated that SWI and conventional MRI sequences have similar efficiency for plaque assessment in MS patients.

In real life conditions, the buyers sometimes pay all or a percentage of the product price before receiving it, and the wholesaler sometimes allows them to prepay it at equal intervals. The present study develops a new mathematical model for products with non-instantaneous deteriorating rates by considering consecutive advance payments. In the proposed inventory model, the shortage is consisting of lost sales along with backorders simultaneously. In addition, the model considers the backlogging as totally dependent on the waiting time for the further cycle. In addition, the appropriate conditions to achieve the optimal solutions have been developed, and numerical instances have been provided to verify and evaluate the results and solution method. The useful methods to effectively reduce the annual total cost are provided according to the results of the sensitivity analysis.

This paper presents a new model for a closed-loop supply chain problem under uncertainty. This model considers production, distribution, collection, recycling and disposal of items simultaneously. Because of the increased importance of the environmental factors, this model focuses on the revers flow of the supply chain and considers different types of technology for recycling centers. The model aims to minimize the establishment cost of centers, shipment cost, holding cost, collection cost and recycling cost. To face with uncertain parameters, a credibility-based possibilistic programming method is applied. Then, a social engineering optimizer algorithm is proposed to solve the problem efficiency. To validate the model and proposed algorithm, the results are compared with the results of GAMS. In addition, they prove the superiority of the proposed algorithm over a genetic algorithm to deal with problems and find better results in less running time. Finally, the behavior of the model is assessed by changing the values of parameters and the results are reported.

Prostate cancer is the most common malignancy in men and the second leading cause of death in all countries of the world. The exact mechanism of prostate cancer is not known. On the other hand, early detection of prostate cancer can lead to a complete cure. Several clinical experiments including Digital Rectum Examination (DRE), biochemistry such as Prostate Specific Antigen (PSA), and pathology such as Trans Rectal Ultra Sonography (TRUS) are used to assess the size and spread of prostate cancer. In this study, the relationship between mean serum PSA and Gleason score as a standard method in patients with prostate cancer was compared using the parameters extracted from DCE MRI.

This applied-fundamental study was performed on 90 patients with prostate cancer, according to McDonaldchr('39')s criteria who were referred to Shafa Imaging Center in Isfahan, from March 2020 to October 2020. Quantitative analysis is based on modeling the change of concentration of the contrast agent using pharmacokinetic modeling techniques. The pathologist then determined the Gleason score using anatomical landmarks (such as prostate urethra) in the same areas suspected of being cancerous. Existing commercial software captures DCE-MRI data and creates parametric maps such as Ktrans and Kep maps that can be used for diagnostic purposes.

Kep and Ktrans maps showed a significant difference between healthy and cancerous tissue. Kep and Ktrans in prostate cancer were significantly higher than in healthy tissue (P<0.05). Pearson correlation coefficient was used to investigate the relationship between DCE-MRI parameters and histopathological findings. No significant relationship was observed between Gleason score and DCE MRI parameters.

DCE MRI parameters significantly improve the accurate diagnosis of prostate cancer and are useful and effective for diagnosis, management, and evaluation of men with prostate cancer, but should not be considered as a substitute for tissue biopsy.

In this paper, a type II robotic mixed-model two-sided assembly line balancing problem is considered. This paper presents a new mixed-integer programming model for type II robotic mixed-model two-sided assembly line balancing to minimize the cycle time, energy consumption, and purchased cost of robots for a given number of workstations. We provided an effective framework for optimizing the multi-objective robotic mixed-model two-sided assembly line balancing problem considering energy consumption and smoothing workload in the make to order environment to help the decision makers make the right decisions under stochastic demand. An augmented epsilon constraint and Lp-metric methods applied to solve the problem, and then, with the help of defining two vertical and horizontal criteria, we attempt to help the decision maker to choose a more efficient solution to make the production line more smooth workload. We demonstrate the efficiency of the proposed method by designing the numerical experiments. ZZ ZZ ZZ ZZ ZZ

This paper presents a scenario-based supply chain network design (SCND) model in the case of disruption occurrence with a single product type. The proposed supply chain (SC) comprises three echelons, including manufacturers, distribution centers (DCs), and customers. Two kinds of DCs, namely reliable and unreliable DCs, are considered in the presented model. Disruption affects unreliable DCs and causes the loss of a portion of their capacity. Thus, an unreliable DC capacity in each period is assumed to be a positive variable specified based on its capacity in the previous period. There are different investment levels for establishing each DC, which affects the amount of capacity loss due to disruption. Two resilience strategies, DC capacity fortification and inventory keeping are considered to reduce the effects of disruption on SC, and the results under each strategy are investigated. The outcomes are investigated by presenting numerical examples, and the advantages of the proactive manner versus reactive manner are shown. Finally, sensitivity analysis is done on disruption related parameters to show how parameters influence the model outputs.

This paper presents a mixed integer non-linear programming (MINLP) model for a bi- objective and multi-depot vehicle routing problem with time windows. The main goals of the paper are minimization of total cost and equitable distribution of commodities between vehicles. Two types of vehicle including delivery and installation vehicles are utilized in the network regarding customers’ needs. Satisfying all demands of the customers is not obligatory and unmet demands are permitted which leads to extra cost. A presented model is applied for real life case study in different provinces of Iran. To tackle the small-size problems, the augmented e-constraint method is utilized by linearization of the model. Because of the NP-hard nature of the problem, as the size of the problem increases, so does the complexity. As such, we develop multi-objective simulated annealing (MOSA meta-heuristic) algorithm for large scale problems. Then, several numerical experiments and sensitivity analyses are conducted to validate the presented model and the solution method, which indicate the efficiency of our proposed approach.

In this study, we discuss a location-inventory-pricing model considering the capacity constraints of the warehouses, disruption, and multiple perishable products. We extend a model that assumes that warehouses may face disruption, failed warehouses cannot cover any service, and their customers are assigned to other warehouses. To decrease the risk of disruption, we examine the efficiency of markup pricing strategy and support services. The objective function of this MINLP is to maximize the total profit of warehouses. To solve this model, Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA) are used. To evaluate the recommended model, several sensitivity analyses are proposed. Finally, the results of numerical experiments implicate the high-performance of GOA in dealing with problems and achieving better results. According to the results, backup services and markup pricing strategies are very effective in reducing the damage caused by the disruption.

Most humanitarian relief items' investigations try to satisfy demands in disaster areas in an appropriate time and reduce the rate of causality. Time is an essential element in humanitarian relief items; the quietest response time, the more rescued people. Reducing response time with high reliability is the main objective of this research. In our investigation, monitoring the route’s situation after occurrence disaster with drones and motorcycles is planned for collecting information about routes and demand points in the first stage. The collected information is analyzed by the disaster management to determine the probability of each scenario. By evaluating collected data, the route repair groups are sent to increase the route’s reliability. In the final step, the relief items operation allocates the relief items to demand points. All in all, this research tries to present a practical model and real situation to survive more people after occurrence disaster. An exact solver solves the evolutionary model in small and medium scales; the developed model in big scale is solved by Grasshopper Optimization Algorithm (GOA), and then results are evaluated. The evaluation results indicate the positive effect of valid initial information on the humanitarian supply chain’s performance.

Multiple sclerosis (MS) is an acute and autoimmune disease that causes inflammation and destruction of myelin in the central nervous system. In this study, the diffusion sequence with different values of b-value was used to determine its optimal value for detection of active plaques.

This cross-sectional study was performed on 90 patients with MS basede on Mc Donald's criteria, referred to the Shafa Imaging Center, Isfahan, Iran. The locations of the plaques and their number were recorded by the radiologist in the relevant checklist. Imaging was performed using the Siemens Avanto system, 1.5 Tesla, with of a standard head coil.

Diffusion weighted images (DWI) with low b-values had a direct and significant relationship with contrast enhancement (CE) method in determining active plaques (P = 0.005). Images with a b-value of 500 had the highest sensitivity (75%) and sensitivity (87.3%) in detecting active plaques.

The results showed that the DWI with the 1.5 Tesla had the ability to distinguish active and inactive plates, because DWI images with low b-values had a direct and significant relationship with CE method in determining active plaques.

In this paper, we develop a multi-period mathematical model involving economic and environmental considerations. A vehicle-routing problem is considered an essential matter due to decreasing the routing cost, especially in the concerned bioenergy supply chain. A few of the optimization model recognized the vehicle routing to design the bioenergy supply chain. In this study, a bi-objective mixed-integer linear programming (MILP) model is presented.  The economic objective function minimizes the transportation, capacity expanding, fixed and variable costs, and the locating routing cost in this problem. The proposed bi-objective model is solved through a Non-Dominated Genetic Algorithm (NSGA- II).  Furthermore, the small-sized problem is solved by the CPLEX solver and augmented ɛ-constraint method.

Cooperative advertising is a cost-sharing mechanism in which a part of retailers' advertising investments are financed by the manufacturers. In recent years, investment among advertising options has become a difficult marketing issue. In this paper, the cooperative advertising problem with advertising options is investigated in a two-period horizon in which the market share in the second period depends on the decisions made in the first period. The problem is solved for two cases of the absence and presence of cooperative advertising contract, and the results are compared. The solution to the problem is presented using the concepts of the Nash equilibrium, Stackelberg game, stochastic games, and dynamic programming. The computational results using numerical examples show that if the cooperative advertising contract is offered in the win-win condition, the players’ profit will increase significantly.

damage caused judicial decisions is one of those cases which although in most cases , causes harm to individuals, but compensation for such damages has a significant bearing on the mental health of individuals in a community and it promotes the health and development of people in society.the victims of the normal damages and specific compensation for damages resulting for judicial decisions is a challenging topic although the current legislation requires it, it does not specify the methods of assessment and has no fixed and definite benchmark. and sometimes it is seen that judicial decisions have caused losses to individuals, but they have not been compensated.in this research, in accordance with the rational principle of the necessity of compensating for the loss and disadvantage of ' la zarar and la zerar' , we have tried to accept that any damage is compensated and should be evaluated and priced and compensated in the best way.

Using second-generation biomass and biofuel deal with environmental pollution and CO2 emissions. Therefore, this paper design an integrated multi-period bi-objective biofuel supply chain network using support vector machine (SVM) and economic analysis to reduce the cost of generating biofuels and CO2 emissions. The economic analysis consists of three scenarios for supplying biomass. The SVM method specifies the potential place to build the bio-refinery. The next step solves the model with the augmented ε-constraint method. Finally, results show that biomass production and imports simultaneously reduce costs by 24.5% compared to the production scenario and 4.3% compared to the import scenario. According to the results obtained, despite the increase in cost, it reduces the amount of CO2 emissions. So, the Pareto solution resulted from the augmented ε-constraint method for the problem is determined as one of the most effective techniques to help the decision-makers.

Non-uniform distribution of customers in a region and variation of their maximum willingness to pay at distinct areas make regional pricing a practical method to maximize the profit of the distribution system. By subtracting the classic objective function, which minimizes operational costs from revenue function, profit maximization is aimed. A distribution network is designed by determining the number of trucks to each established distribution center, allocating customers in routes, and inventory levels of customers. Also, environmental impacts, including fuel consumption and CO2 emission, aimed to be minimized. So, a new quadratic mixed-integer programming model is presented for the Green Transportation Location-Inventory-Routing Problem integrated with dynamic regional pricing problem (GTLIRP+DRP). The model is applied to the real case study, to show its competent application. To tackle this problem, a Hybrid Bees Algorithm (HBA) is developed and verified by the genetic algorithm. Finally, managers suggested using HBA that achieves better solutions in the less computational time.

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

This study proposes a novel option-revenue sharing coordination contract framework. In the proposed model, the retailer determines the number of order sales effort. The manufacturer sets the price of products for the wholesale strategy. The investigated supply chain problem analyzes the results of different strategies. In the proposed coordination contract problem, two types of games including retailer-based game and manufacturer-based Stackelberg game are considered. In both cases the retailer adopts the value of order and the sales effort and the manufacturer determines the wholesale price. To assess the performance of the proposed contract, a wholesale and a basic selection contract are considered in the model. To obtain the Nash equilibrium in the retailer-based state of the proposed option-revenue sharing coordination contract problem, a hybrid algorithm consisting of a heuristic and a genetic algorithm is proposed by considering the computational complexities of the proposed model. A numerical comparison between the proposed contract and other cases demonstrates that the option-revenue sharing contract significantly dominates the basic option and the wholesale price contract. Finally, we implemented some numerical experiments on the critical parameters of the contract. Based on the results, increasing the price-dependency of demand results in less number of products ordered by the retailer.

This paper presents a novel formulation of the integrated bi-objective problem of project selection and scheduling. The first objective is to minimize the aggregated risk by evaluating the expected value of schedule delay and the second objective is to maximize the achieved benefit. To evaluate the expected aggregated impacts of risks, an objective function based on the Bayesian Networks is proposed. In the extant mathematical models of the joint problem of project selection and scheduling, projects are selected and scheduled without considering the risk network of the projects indicating the individual and interaction effects of risks impressing the duration of the activities. To solve the model, two solution approaches have been developed, one exact and one metaheuristic approach. Goal Programming method is used to optimally select and schedule projects. Since the problem is NP hard, an algorithm, named GPGA, which combines Goal Programming method and Genetic Algorithm is proposed. Finally, the efficiency of the proposed algorithm is assessed not only based on small size instances but also by generating and testing representative datasets of larger instances. The results of the computational experiments indicate that it has acceptable performance to handle large size and more realistic problems.